# Overview

[8-bit optimizers](https://hf.co/papers/2110.02861) reduce the memory footprint of 32-bit optimizers without any performance degradation which means you can train large models with many parameters faster. At the core of 8-bit optimizers is block-wise quantization which enables quantization accuracy, computational efficiency, and stability.

bitsandbytes provides 8-bit optimizers through the base [`Optimizer8bit`] class, and additionally provides [`Optimizer2State`] and [`Optimizer1State`] for 2-state (for example, [`Adam`]) and 1-state (for example, [`Adagrad`]) optimizers respectively. To provide custom optimizer hyperparameters, use the [`GlobalOptimManager`] class to configure the optimizer.

## Optimizer8bit

[[autodoc]] bitsandbytes.optim.optimizer.Optimizer8bit
    - __init__

## Optimizer2State

[[autodoc]] bitsandbytes.optim.optimizer.Optimizer2State
    - __init__

## Optimizer1State

[[autodoc]] bitsandbytes.optim.optimizer.Optimizer1State
    - __init__

## Utilities

[[autodoc]] bitsandbytes.optim.optimizer.GlobalOptimManager